1. Technical Field
This invention relates to waveform analysis and more particularly to a system for performing real-time waveform analysis using artificial neural networks.
2. Discussion
Accurate waveform analysis plays a crucial role in many fields. That is, many technical areas involve problems in analyzing an incoming signal having a characteristic waveform which must be interpreted and classified. Some examples of such signals include the analysis of audio, sonar, and electromagnetic waves from various sources. For example, detailed analysis and classification of speech signals is required to perform automated speech recognition. Another example occurs in analysis of seismographic signals. Numerous waveform analysis applications exist in the medical field. Examples of physiologic waveforms which are subject to analysis include respiratory wave analysis including pressure and gas concentrations, vascular pressure waves (arterial, etc.), airway pressure waves; electrocardiograms; electroencephalograms; pressure wave analysis in a cardiopulmonary bypass machine circuit, plethysmograms and bioimpedance waves.
Some of the more difficult tasks involved in analyzing such signals include front end (preprocessing) steps, including waveform segmentation; classification of waveforms into predetermined classes; and interpreting of ambiguous data such as ambiguous classification results. Difficulties in pedorming tasks such as these are compounded because of wide variations in such waveforms due to countless factors causing the signal to deviate in frequency, amplitude, shape, etc. from expected norms. Because of these variations, the task of developing algorithms and writing software to automatically pedorm waveform analysis frequently becomes a daunting, monumental task. This "software bottleneck" has slowed the development of systems in this area. Even when large sums of time and money are spent to develop such software algorithms, results in many areas are still not satisfactory.
A further problem with conventional approaches is that they are frequently computationally expensive which either necessitates enormous computing power, or simply precludes their use in systems which must perform in real-time. For example, in the analysis of physiological waveforms, real time pedormance is often essential to achieve results fast enough to take corrective responses to abnormalities in a patient's physiological functioning.
A promising alternative to conventional symbolic sequential computing techniques is offered by artificial neural network systems. Neural network architectures, which are loosely based on knowledge of the neuroanatomy of the brain, have been shown to perform well at tasks such as classification of waveforms having subtle differences - tasks which heretofore have been limited to performance by humans. In addition to their robust ability to recognize characteristic waveforms which vary widely from predicted shapes, neural networks also promise to offer a solution to the "software bottleneck" discussed above. This is because explicit algorithms need not be developed for neural networks to recognize waveforms. Instead, these systems, trained with exemplars, converge to an acceptable solution. In addition, once trained, a neural network can generally perform a recognition task rapidly due to its inherent parallelism. See R. P. Lippmann "An Introduction to Computing with Neural Networks" IEEE ASSP Magazine, April 1987, page 4 for a discussion of the better-known neural network architectures and techniques. Despite the promising outlook, many difficult problem remain to be solved in such areas as preprocessing data to make it suitable for processing by the neural network, optimizing the architectures and learning paradigms for the recognition process, as well as postprocessing and interpretation of ambiguous results.
Thus it would be desirable to provide a system and method to perform waveform analysis which overcomes some or all of these shortcomings. It would be desirable to have a system which can perform accurate analysis and classification of waveforms without requiring extensive algorithm and software development time. Further, it would be desirable to provide such a system which can perform waveform analysis in real-time with readily available and reasonably priced hardware. It would further be desirable to provide a waveform analysis system which can readily analyze and classify waveforms which deviate substantially from predicted patterns. Further, it would be desirable to provide a waveform analysis system which makes use of the advantages of neural network architectures and also resolves inherent preprocessing, analysis and postprocessing difficulties.